Search results for "Segmentation"

showing 10 items of 674 documents

Harmonized benchmark labels of the hippocampus on magnetic resonance: The EADC-ADNI project

2015

Abstract Background A globally harmonized protocol (HarP) for manual hippocampal segmentation based on magnetic resonance has been recently developed by a task force from European Alzheimer's Disease Consortium (EADC) and Alzheimer's Disease Neuroimaging Initiative (ADNI). Our aim was to produce benchmark labels based on the HarP for manual segmentation. Methods Five experts of manual hippocampal segmentation underwent specific training on the HarP and segmented 40 right and left hippocampi from 10 ADNI subjects on both 1.5 T and 3 T scans. An independent expert visually checked segmentations for compliance with the HarP. Descriptive measures of agreement between tracers were intraclass cor…

MaleInservice TrainingEpidemiologyIntraclass correlationNeuroimagingHippocampusCellular and Molecular Neuroscienceddc:616.89Imaging Three-DimensionalDevelopmental NeuroscienceNeuroimagingSimilarity (network science)Alzheimer DiseasemedicineImage Processing Computer-AssistedHumansSegmentationCognitive DysfunctionHARPAgedAged 80 and overmedicine.diagnostic_testbusiness.industryHealth PolicyReproducibility of ResultsMagnetic resonance imagingPattern recognitionOrgan SizeMagnetic Resonance ImagingConfidence intervalPsychiatry and Mental healthBenchmark (computing)FemaleNeurology (clinical)Artificial intelligenceGeriatrics and GerontologyAtrophybusinessPsychologyNeuroscience
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Three-Dimensional Reconstruction of the Bony Nasolacrimal Canal by Automated Segmentation of Computed Tomography Images.

2016

Objective To apply a fully automated method to quantify the 3D structure of the bony nasolacrimal canal (NLC) from CT scans whereby the size and main morphometric characteristics of the canal can be determined. Design Cross-sectional study. Subjects 36 eyes of 18 healthy individuals. Methods Using software designed to detect the boundaries of the NLC on CT images, 36 NLC reconstructions were prepared. These reconstructions were then used to calculate NLC volume. The NLC axis in each case was determined according to a polygonal model and to 2nd, 3rd and 4th degree polynomials. From these models, NLC sectional areas and length were determined. For each variable, descriptive statistics and nor…

MaleModels AnatomicCritical Care and Emergency Medicinelcsh:MedicineComputed tomographyPolynomialsDiagnostic RadiologyNormality test0302 clinical medicineMedicine and Health SciencesSegmentationDegree of a polynomiallcsh:ScienceTomographyMusculoskeletal SystemTrauma MedicineMathematicsMultidisciplinaryNasolacrimal ductmedicine.diagnostic_testRadiology and ImagingAnatomyMiddle Agedmedicine.anatomical_structureSurgery Computer-AssistedPhysical SciencesNasolacrimal canalFemaleAnatomyResearch ArticleAdultComputer and Information SciencesImaging TechniquesTrauma SurgeryAutomated segmentationNeuroimagingSurgical and Invasive Medical ProceduresResearch and Analysis MethodsBone and BonesComputer Software03 medical and health sciencesImaging Three-DimensionalDiagnostic MedicinemedicineHumansSkeletonAgedMorphometrySkulllcsh:RBiology and Life SciencesComputing MethodsComputed Axial TomographyCross-Sectional StudiesAlgebra030221 ophthalmology & optometrylcsh:QTomography X-Ray ComputedNasolacrimal DuctMathematics030217 neurology & neurosurgeryNeuroscienceBiomedical engineeringVolume (compression)PLoS ONE
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Spectral clustering of shape and probability prior models for automatic prostate segmentation.

2013

Imaging artifacts in Transrectal Ultrasound (TRUS) images and inter-patient variations in prostate shape and size challenge computer-aided automatic or semi-automatic segmentation of the prostate. In this paper, we propose to use multiple mean parametric models derived from principal component analysis (PCA) of shape and posterior probability information to segment the prostate. In contrast to traditional statistical models of shape and intensity priors, we use posterior probability of the prostate region determined from random forest classification to build, initialize and propagate our model. Multiple mean models derived from spectral clustering of combined shape and appearance parameters…

MaleModels StatisticalComputer scienceSegmentation-based object categorizationbusiness.industryPosterior probabilityProstateScale-space segmentationReproducibility of ResultsPattern recognitionImage segmentationModels BiologicalSensitivity and SpecificitySpectral clusteringPattern Recognition AutomatedPoint distribution modelSubtraction TechniqueImage Interpretation Computer-AssistedHumansComputer visionSegmentationComputer SimulationArtificial intelligencebusinessUltrasonographyAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance : Evidence of validity

2014

BackgroundAn international Delphi panel has defined a harmonized protocol (HarP) for the manual segmentation of the hippocampus on MR. The aim of this study is to study the concurrent validity of the HarP toward local protocols, and its major sources of variance.MethodsFourteen tracers segmented 10 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases scanned at 1.5 T and 3T following local protocols, qualified for segmentation based on the HarP through a standard web-platform and resegmented following the HarP. The five most accurate tracers followed the HarP to segment 15 ADNI cases acquired at three time points on both 1.5 T and 3T.ResultsThe agreement among tracers was relatively low…

MalePathologyDiagnostic criteriaEpidemiologyImage Processinggenetics [Alzheimer Disease]HippocampusFunctional LateralityImagingpathology [Alzheimer Disease]ddc:616.89methods [Magnetic Resonance Imaging]Computer-AssistedClinical trialsddc:150methods [Image Processing Computer-Assisted]ValidationImage Processing Computer-AssistedSegmentationHARPmedicine.diagnostic_testHealth PolicyOrgan SizeAlzheimer's diseaseMiddle Agedinstrumentation [Magnetic Resonance Imaging]Manual segmentationMagnetic Resonance ImagingPsychiatry and Mental healthMagnetic resonanceBiomedical ImagingManual segmentationFemalemethods [Neuroimaging]methods [Imaging Three-Dimensional]EADC-ADNI Working Group on The Harmonized Protocol for Manual Hippocampal Volumetry and for the Alzheimer's Disease Neuroimaging Initiativemedicine.medical_specialtyHippocampal volumetry; Magnetic resonance; Alzheimer's disease; Biomarkers; Diagnostic criteria; Enrichment; Clinical trials; Validation; Harmonized protocol; Standard operating procedures; Manual segmentationConcurrent validityClinical SciencesHarmonized protocolNeuroimagingArticleHippocampal volumetryCellular and Molecular NeuroscienceImaging Three-DimensionalDevelopmental NeuroscienceNeuroimagingClinical ResearchAlzheimer DiseasemedicineHumansddc:610AgedProtocol (science)ReproducibilityInternetbusiness.industryNeurosciencesReproducibility of ResultsMagnetic resonance imagingBrain DisordersStandard operating procedurespathology [Hippocampus]EnrichmentGeriatricsThree-DimensionalNeurology (clinical)Geriatrics and GerontologyAtrophyNuclear medicinebusinessBiomarkers
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SEMI-AUTOMATIC VOLUMETRIC SEGMENTATION OF THE UPPER AIRWAYS IN PATIENTS WITH PIERRE ROBIN SEQUENCE

2014

Pierre Robin malformation is a rare craniofacial dysmorphism whose pathogenesis is multifactorial. Although there is some agreement in non-invasive treatment in less severe cases, the dispute is still open on cases with severe respiratory impairment. We present a semi-automatic novel diagnostic tool for calculating upper airway volume, in order to eventually address surgery in patients with Pierre Robin Sequence (PRS). Multidetector CT datasets of two patients and two controls were tested to assess the proposed method for ROI segmentation, upper airway volume computation and three-dimensional reconstructions. The experimental results show an irregular pattern and a severely reduced cross-s…

MalePathologymedicine.medical_specialtymultidetector CTJaccard indexMultidetector ctImaging Three-DimensionalSimilarity (network science)Multidetector Computed TomographyImage Processing Computer-AssistedMedicineHumansRadiology Nuclear Medicine and imagingIn patientSegmentationairway model reconstructionRobin SequencePierre Robin sequenceAnatomy Cross-SectionalPierre Robin Syndromebusiness.industryairways segmentationInfantGeneral MedicineOriginal ArticlesOrgan SizePIERRE ROBIN SEQUENCE MULTIDETECTOR CT3D renderingAirway ObstructionRegion growingCase-Control StudiesPharynxFemaleNeurology (clinical)LarynxAirwaybusinessNuclear medicineregion growing
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Mid-sagittal plane detection for advanced physiological measurements in brain scans

2019

Objective The process of diagnosing many neurodegenerative diseases, such as Parkinson's and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set. Approach To improve the identification of the mid-sagittal plane we have developed an algorithm in Matlab® based on the k-means clustering function. The results have been compared …

MalePhysiologyComputer scienceBiomedical EngineeringBiophysicsk-means algorithmNeuroimagingSpatial reference systemPhysiology (medical)medicinemid-sagittal planeHumansmagnetic resonance imagingCluster analysisSettore MAT/07 - Fisica MatematicaAgedImage segmentationmedicine.diagnostic_testbusiness.industryk-means clusteringBrainMagnetic resonance imagingPattern recognitionGold standard (test)Image segmentationMiddle AgedReference StandardsSagittal planemedicine.anatomical_structuremachine learningDatabases as TopicFemaleArtificial intelligencebusinessAlgorithms
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A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method

2020

AbstractBackgroundPositron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure.This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between p…

MalePositron emission tomographyComputer scienceLesion volumelcsh:Computer applications to medicine. Medical informaticsBiochemistry030218 nuclear medicine & medical imagingLesion03 medical and health sciences0302 clinical medicineRadiomicsStructural BiologyArtificial IntelligencemedicineHumansSegmentationNeoplasm Metastasislcsh:QH301-705.5Molecular BiologyCancerActive contour modelRadiomicsmedicine.diagnostic_testBrain Neoplasmsbusiness.industryApplied MathematicsResearchCancerPattern recognitionMiddle AgedPrognosismedicine.diseaseComputer Science ApplicationsCancer treatmentBiological target volumelcsh:Biology (General)Positron emission tomographyFeature (computer vision)030220 oncology & carcinogenesisPositron-Emission TomographyFully automaticlcsh:R858-859.7FemaleActive contourArtificial intelligencemedicine.symptomRadiomicActive contour; Biological target volume; Cancer; Positron emission tomography; Radiomics.businessBMC Bioinformatics
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Joint Probability of Shape and Image Similarities to Retrieve 2D TRUS-MR Slice Correspondence for Prostate Biopsy

2012

International audience; This paper presents a novel method to identify the 2D axial Magnetic Resonance (MR) slice from a pre-acquired MR prostate volume that closely corresponds to the 2D axial Transrectal Ultrasound (TRUS) slice obtained during prostate biopsy. The method combines both shape and image intensity information. The segmented prostate contours in both the imaging modalities are described by shape-context representations and matched using the Chi-square distance. Normalized mutual information and correlation coefficient between the TRUS and MR slices are computed to find image similarities. Finally, the joint probability values comprising shape and image similarities are used in…

MaleProstate biopsyBiopsy[INFO.INFO-IM] Computer Science [cs]/Medical Imaging030230 surgeryNormalized mutual information030218 nuclear medicine & medical imagingImage (mathematics)03 medical and health sciences0302 clinical medicineJoint probability distribution[INFO.INFO-IM]Computer Science [cs]/Medical ImagingMedicineHumansComputer visionMR ProstateProbabilitymedicine.diagnostic_test[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industryUltrasoundProstatic NeoplasmsMagnetic resonance imagingImage segmentationMagnetic Resonance ImagingArtificial intelligencebusiness
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Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge

2014

Contains fulltext : 137969.pdf (Publisher’s version ) (Open Access) Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or …

MaleScannerObserver (quantum physics)Computer scienceHealth InformaticsSensitivity and SpecificityArticleProstate cancerSegmentationImaging Three-DimensionalRobustness (computer science)Image Interpretation Computer-AssistedmedicineHumansRadiology Nuclear Medicine and imagingSegmentationChallengeProtocol (science)Modality (human–computer interaction)Radiological and Ultrasound TechnologyProstateProstatic NeoplasmsReproducibility of ResultsReference Standardsmedicine.diseaseImage EnhancementComputer Graphics and Computer-Aided DesignMagnetic Resonance ImagingActive appearance modelUrological cancers Radboud Institute for Health Sciences [Radboudumc 15]Computer Vision and Pattern RecognitionArtifactsAlgorithmAlgorithmsRare cancers Radboud Institute for Health Sciences [Radboudumc 9]MRI
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Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

2020

In this paper, we present an evaluation of four encoder&ndash

MaleSimilarity (geometry)Computer scienceSegNet02 engineering and technologylcsh:Chemical technologyBiochemistryArticleencoder–decoder030218 nuclear medicine & medical imagingAnalytical Chemistry03 medical and health sciencesProstate cancer0302 clinical medicineProstateImage Processing Computer-Assisted0202 electrical engineering electronic engineering information engineeringmedicineHumanslcsh:TP1-1185SegmentationElectrical and Electronic EngineeringInstrumentationmedicine.diagnostic_testPixelbusiness.industryProstateCNNsPattern recognitionMagnetic resonance imagingFCNmedicine.diseaseMagnetic Resonance ImagingU-NetAtomic and Molecular Physics and OpticsSemanticsIntensity normalizationmedicine.anatomical_structureDeepLabV3+Deep neural networks020201 artificial intelligence & image processingNeural Networks ComputerArtificial intelligencebusinessDNNSensors
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